MDL-CW: A Multimodal Deep Learning Framework With Cross Weights

no code implementations • CVPR 2016 • Sarah Rastegar, Mahdieh Soleymani, Hamid R. Rabiee, Seyed Mohsen Shojaee

In this paper, we propose a multimodal deep learning framework (MDL-CW) that exploits the cross weights between representation of modalities, and try to gradually learn interactions of the modalities in a deep network manner (from low to high level interactions).

Attribute Information Retrieval +4

Semi-supervised Zero-Shot Learning by a Clustering-based Approach

no code implementations • 29 May 2016 • Seyed Mohsen Shojaee, Mahdieh Soleymani Baghshah

We seek a linear transformation on signatures to map them onto the visual features, such that the mapped signatures of the seen classes are close to labeled samples of the corresponding classes and unlabeled data are also close to the mapped signatures of one of the unseen classes.

Clustering Object Recognition +1